RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: Comparing 9-1-1 and Social Media

飓风哈维“呼救”性质的变化：911和社交媒体哈维飓风是第一个大数据灾难，社交媒体“呼救”似乎取代了过载的911呼叫系统；社交媒体提供了一个可见的对话链接来提供帮助。但这种在公共社交媒体上寻求帮助的行为形式相对较新。该项目(1)捕捉飓风受害者和应急响应工作者（政府和志愿者）的声音(2)使用捕获的数据来描述实际社交媒体呼救中存在的语言特征，以及(3)将大数据方法应用于新的紧急情况，以评估该情况的呼救。该项目为第一响应者在未来紧急情况调度时如何利用社交媒体和传统911的新思路铺平了道路。目前危机信息学文献的做法是围绕与灾难相关的关键词，在灾难/善后期间挖掘社交媒体数据。然而，这样的数据收集囊括了一切——从募捐到新闻报道——在如此广泛的数据集中过滤信号是一项挑战。重要的是确定灾害受害者在公开“呼救”时使用的语言的共同线索，使应急管理人员能够通过各种沟通渠道迅速查明这些需求并挽救生命。这个项目的方法是独特的，因为组合方法通过理解灾民在请求帮助时使用的特定关键词来隔离灾民在社交媒体上的对话信号。通过对“哈维”和“厄玛”飓风受害者、应急响应组织以及德克萨斯州/卡津海军等组织（通过社交媒体组织工作的志愿者团体）的实地采访和调查，该项目将描述发布的内容、发布的求助信息以及这些请求如何产生响应。面试协议将引出受访者的社交媒体帖子的例子，以帮助开发这些内容的本体。结合将购买的多个平台（YouTube、Twitter、Reddit和Facebook）的历史数据，该项目的第二阶段将匹配精确的搜索查询（使用布尔运算符缩小范围）。搜索机制将由受害者的社交媒体行为和他们对哈维和厄玛的具体经历的语言驱动，而不是涵盖所有的标签和搜索词。这些类型的受害者驱动的本体论是围绕灾难的具体经验发展起来的，严重缺乏和研究不足。

RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: 9-1-1 and Social MediaHurricane Harvey is the first big-data disaster where social media "calls" for help appear to have supplanted the overloaded 9-1-1 call systems; social media provided a visible, dialogic link to help. But this form of help-seeking behavior on public social media is relatively new. This project (1) captures the voices of hurricane victims and emergency response workers (both governmental and volunteer) (2) uses captured data to characterize the language present in actual social media calls for help, and (3) applies a big-data approach to a new emergency situation to assess that situation's calls for help. This project paves the way for new ways of thinking about how first-responders can utilize social media alongside traditional 9-1-1 when dispatching in future emergencies. The current practice in the crisis informatics literature is to mine social-media data during the disaster/aftermath around disaster-related keywords. However, such data collection pulls in everything--from solicitations for donations, to news stories--and it is challenging to filter signal from noise in such broad data sets. It is important to identify common threads in the language disaster victims use in their public "calls for help" to allow emergency managers to rapidly pinpoint these needs across varied communication channels and save lives. The approach in this project is unique because the combinatorial method isolates the signal of conversations by disaster victims on social media by understanding the specific keywords disaster victims use when requesting help. Using field interviews and surveys with Harvey and Irma victims, emergency response organizations, and organizations like the Texas/Cajun Navy--volunteer groups who organized their efforts through social media--the project will characterize what was posted, where calls for help were posted, and how these requests generated responses. The interview protocol will elicit examples of interviewees' social media posts to help develop ontologies of this content. In combination with historical data across several platforms (YouTube, Twitter, Reddit and Facebook) that will be purchased, the second phase of this project will match precise search queries (narrowed using boolean operators). The search mechanism will be driven by victims' social media behaviors and language specific to their experience of Harvey and Irma, rather than catchall hashtags and search terms. These types of victim-driven ontologies developed around specific experiences of a disaster are seriously lacking and understudied.